Overview

Dataset statistics

Number of variables14
Number of observations243
Missing cells207
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.1 KiB
Average record size in memory118.5 B

Variable types

Text5
Categorical5
Numeric4

Dataset

Description대전광역시 불법주정차 단속구간 고정형 CCTV 단속구간(관리번호, 시군구명, 시군구코드, 우편번호, 주소, 연락처, 위도, 경도, 위치, 교차로수, 대표이미지, 기준일자, 기타, CCTV수, 위치정보)
URLhttps://www.data.go.kr/data/15076794/fileData.do

Alerts

시군구명 is highly overall correlated with 우편번호 and 5 other fieldsHigh correlation
시군구코드 is highly overall correlated with 우편번호 and 5 other fieldsHigh correlation
우편번호 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 우편번호 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 시군구명 and 1 other fieldsHigh correlation
연락처 is highly overall correlated with 시군구명 and 2 other fieldsHigh correlation
기준일자 is highly overall correlated with 시군구명 and 2 other fieldsHigh correlation
위치 has 3 (1.2%) missing valuesMissing
기타 has 201 (82.7%) missing valuesMissing
관리번호 has unique valuesUnique
교차로수 has 50 (20.6%) zerosZeros

Reproduction

Analysis started2023-12-12 19:07:47.535670
Analysis finished2023-12-12 19:07:51.187453
Duration3.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct243
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T04:07:51.375494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters4374
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique243 ?
Unique (%)100.0%

Sample

1st row3680000_CCTV_00019
2nd row3680000_CCTV_00020
3rd row3680000_CCTV_00021
4th row3680000_CCTV_00024
5th row3680000_CCTV_00026
ValueCountFrequency (%)
3680000_cctv_00019 1
 
0.4%
3650000_cctv_00229 1
 
0.4%
3660000_cctv_00077 1
 
0.4%
3660000_cctv_00095 1
 
0.4%
3660000_cctv_00076 1
 
0.4%
3660000_cctv_00086 1
 
0.4%
3660000_cctv_00096 1
 
0.4%
3660000_cctv_00085 1
 
0.4%
3660000_cctv_00078 1
 
0.4%
3660000_cctv_00075 1
 
0.4%
Other values (233) 233
95.9%
2023-12-13T04:07:51.807829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1610
36.8%
_ 486
 
11.1%
C 486
 
11.1%
6 357
 
8.2%
3 297
 
6.8%
T 243
 
5.6%
V 243
 
5.6%
1 154
 
3.5%
7 113
 
2.6%
2 100
 
2.3%
Other values (4) 285
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2916
66.7%
Uppercase Letter 972
 
22.2%
Connector Punctuation 486
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1610
55.2%
6 357
 
12.2%
3 297
 
10.2%
1 154
 
5.3%
7 113
 
3.9%
2 100
 
3.4%
5 87
 
3.0%
4 82
 
2.8%
8 72
 
2.5%
9 44
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
C 486
50.0%
T 243
25.0%
V 243
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3402
77.8%
Latin 972
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1610
47.3%
_ 486
 
14.3%
6 357
 
10.5%
3 297
 
8.7%
1 154
 
4.5%
7 113
 
3.3%
2 100
 
2.9%
5 87
 
2.6%
4 82
 
2.4%
8 72
 
2.1%
Latin
ValueCountFrequency (%)
C 486
50.0%
T 243
25.0%
V 243
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1610
36.8%
_ 486
 
11.1%
C 486
 
11.1%
6 357
 
8.2%
3 297
 
6.8%
T 243
 
5.6%
V 243
 
5.6%
1 154
 
3.5%
7 113
 
2.6%
2 100
 
2.3%
Other values (4) 285
 
6.5%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
서구
71 
유성구
69 
중구
43 
동구
32 
대덕구
28 

Length

Max length3
Median length2
Mean length2.399177
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대덕구
2nd row대덕구
3rd row대덕구
4th row대덕구
5th row대덕구

Common Values

ValueCountFrequency (%)
서구 71
29.2%
유성구 69
28.4%
중구 43
17.7%
동구 32
13.2%
대덕구 28
 
11.5%

Length

2023-12-13T04:07:52.012936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:07:52.155779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 71
29.2%
유성구 69
28.4%
중구 43
17.7%
동구 32
13.2%
대덕구 28
 
11.5%

시군구코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
3660000
71 
3670000
69 
3650000
43 
3640000
32 
3680000
28 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3680000
2nd row3680000
3rd row3680000
4th row3680000
5th row3680000

Common Values

ValueCountFrequency (%)
3660000 71
29.2%
3670000 69
28.4%
3650000 43
17.7%
3640000 32
13.2%
3680000 28
 
11.5%

Length

2023-12-13T04:07:52.305022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:07:52.456266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3660000 71
29.2%
3670000 69
28.4%
3650000 43
17.7%
3640000 32
13.2%
3680000 28
 
11.5%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)71.5%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean34681.562
Minimum28475
Maximum35412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T04:07:52.594350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28475
5-th percentile34049
Q134199
median34649.5
Q335225
95-th percentile35362
Maximum35412
Range6937
Interquartile range (IQR)1026

Descriptive statistics

Standard deviation618.42329
Coefficient of variation (CV)0.017831472
Kurtosis40.640185
Mean34681.562
Median Absolute Deviation (MAD)471
Skewness-4.1544855
Sum8392938
Variance382447.37
MonotonicityNot monotonic
2023-12-13T04:07:52.786384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34138 5
 
2.1%
34137 5
 
2.1%
34086 5
 
2.1%
34551 4
 
1.6%
34627 4
 
1.6%
34629 3
 
1.2%
34030 3
 
1.2%
35329 3
 
1.2%
34202 3
 
1.2%
34185 3
 
1.2%
Other values (163) 204
84.0%
ValueCountFrequency (%)
28475 1
 
0.4%
34009 2
0.8%
34016 1
 
0.4%
34019 2
0.8%
34030 3
1.2%
34032 2
0.8%
34049 3
1.2%
34050 1
 
0.4%
34052 1
 
0.4%
34068 1
 
0.4%
ValueCountFrequency (%)
35412 1
 
0.4%
35397 1
 
0.4%
35394 1
 
0.4%
35383 1
 
0.4%
35382 2
0.8%
35378 1
 
0.4%
35368 3
1.2%
35367 1
 
0.4%
35366 1
 
0.4%
35362 2
0.8%

주소
Text

Distinct242
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T04:07:53.150516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length26.884774
Min length15

Characters and Unicode

Total characters6533
Distinct characters302
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)99.2%

Sample

1st row대전광역시 대덕구 신탄진북로 33
2nd row대전광역시 대덕구 대덕대로 1591
3rd row대전광역시 대덕구 계족산로81번길
4th row대전광역시 대덕구 중리남로 39
5th row대전광역시 대덕구 한밭대로1024번길 20
ValueCountFrequency (%)
대전광역시 243
 
20.9%
서구 71
 
6.1%
유성구 69
 
5.9%
중구 43
 
3.7%
앞 34
 
2.9%
동구 32
 
2.8%
대덕구 28
 
2.4%
궁동 9
 
0.8%
대덕대로 8
 
0.7%
6
 
0.5%
Other values (498) 619
53.3%
2023-12-13T04:07:53.645305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
919
 
14.1%
대 364
 
5.6%
전 275
 
4.2%
시 256
 
3.9%
구 256
 
3.9%
역 247
 
3.8%
광 245
 
3.8%
로 207
 
3.2%
동 206
 
3.2%
1 204
 
3.1%
Other values (292) 3354
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4088
62.6%
Decimal Number 986
 
15.1%
Space Separator 919
 
14.1%
Close Punctuation 203
 
3.1%
Open Punctuation 203
 
3.1%
Dash Punctuation 79
 
1.2%
Other Punctuation 36
 
0.6%
Uppercase Letter 17
 
0.3%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 364
 
8.9%
전 275
 
6.7%
시 256
 
6.3%
구 256
 
6.3%
역 247
 
6.0%
광 245
 
6.0%
로 207
 
5.1%
동 206
 
5.0%
성 91
 
2.2%
서 90
 
2.2%
Other values (263) 1851
45.3%
Uppercase Letter
ValueCountFrequency (%)
C 4
23.5%
K 2
11.8%
M 2
11.8%
B 2
11.8%
J 1
 
5.9%
V 1
 
5.9%
G 1
 
5.9%
U 1
 
5.9%
L 1
 
5.9%
A 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 204
20.7%
2 114
11.6%
3 106
10.8%
5 105
10.6%
4 91
9.2%
0 82
8.3%
6 80
 
8.1%
7 75
 
7.6%
8 66
 
6.7%
9 63
 
6.4%
Other Punctuation
ValueCountFrequency (%)
/ 30
83.3%
, 5
 
13.9%
@ 1
 
2.8%
Space Separator
ValueCountFrequency (%)
919
100.0%
Close Punctuation
ValueCountFrequency (%)
) 203
100.0%
Open Punctuation
ValueCountFrequency (%)
( 203
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4088
62.6%
Common 2426
37.1%
Latin 19
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 364
 
8.9%
전 275
 
6.7%
시 256
 
6.3%
구 256
 
6.3%
역 247
 
6.0%
광 245
 
6.0%
로 207
 
5.1%
동 206
 
5.0%
성 91
 
2.2%
서 90
 
2.2%
Other values (263) 1851
45.3%
Common
ValueCountFrequency (%)
919
37.9%
1 204
 
8.4%
) 203
 
8.4%
( 203
 
8.4%
2 114
 
4.7%
3 106
 
4.4%
5 105
 
4.3%
4 91
 
3.8%
0 82
 
3.4%
6 80
 
3.3%
Other values (7) 319
 
13.1%
Latin
ValueCountFrequency (%)
C 4
21.1%
e 2
10.5%
K 2
10.5%
M 2
10.5%
B 2
10.5%
J 1
 
5.3%
V 1
 
5.3%
G 1
 
5.3%
U 1
 
5.3%
L 1
 
5.3%
Other values (2) 2
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4088
62.6%
ASCII 2445
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
919
37.6%
1 204
 
8.3%
) 203
 
8.3%
( 203
 
8.3%
2 114
 
4.7%
3 106
 
4.3%
5 105
 
4.3%
4 91
 
3.7%
0 82
 
3.4%
6 80
 
3.3%
Other values (19) 338
 
13.8%
Hangul
ValueCountFrequency (%)
대 364
 
8.9%
전 275
 
6.7%
시 256
 
6.3%
구 256
 
6.3%
역 247
 
6.0%
광 245
 
6.0%
로 207
 
5.1%
동 206
 
5.0%
성 91
 
2.2%
서 90
 
2.2%
Other values (263) 1851
45.3%

연락처
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
042-609-2852
76 
042-270-5365
48 
042-611-2574
47 
042-606-6885
43 
042-608-5283
28 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row042-608-5283
2nd row042-608-5283
3rd row042-608-5283
4th row042-608-5283
5th row042-608-5283

Common Values

ValueCountFrequency (%)
042-609-2852 76
31.3%
042-270-5365 48
19.8%
042-611-2574 47
19.3%
042-606-6885 43
17.7%
042-608-5283 28
 
11.5%
042-611-2565 1
 
0.4%

Length

2023-12-13T04:07:53.838781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:07:53.960577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
042-609-2852 76
31.3%
042-270-5365 48
19.8%
042-611-2574 47
19.3%
042-606-6885 43
17.7%
042-608-5283 28
 
11.5%
042-611-2565 1
 
0.4%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct240
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.348822
Minimum36.296081
Maximum36.451719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T04:07:54.102200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.296081
5-th percentile36.305383
Q136.325326
median36.347741
Q336.361233
95-th percentile36.424481
Maximum36.451719
Range0.15563769
Interquartile range (IQR)0.035907145

Descriptive statistics

Standard deviation0.033883943
Coefficient of variation (CV)0.0009321882
Kurtosis1.5640154
Mean36.348822
Median Absolute Deviation (MAD)0.01882809
Skewness1.1816484
Sum8832.7638
Variance0.0011481216
MonotonicityNot monotonic
2023-12-13T04:07:54.620675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.30254481 2
 
0.8%
36.42460315 2
 
0.8%
36.34010204 2
 
0.8%
36.35054146 1
 
0.4%
36.34644671 1
 
0.4%
36.34729019 1
 
0.4%
36.34821766 1
 
0.4%
36.35158737 1
 
0.4%
36.36953479 1
 
0.4%
36.36191503 1
 
0.4%
Other values (230) 230
94.7%
ValueCountFrequency (%)
36.2960811 1
0.4%
36.29671663 1
0.4%
36.29672867 1
0.4%
36.2992688 1
0.4%
36.29978347 1
0.4%
36.2999772 1
0.4%
36.30100731 1
0.4%
36.30254481 2
0.8%
36.30430792 1
0.4%
36.30446041 1
0.4%
ValueCountFrequency (%)
36.45171879 1
0.4%
36.45171159 1
0.4%
36.45121274 1
0.4%
36.45094809 1
0.4%
36.45050677 1
0.4%
36.45021638 1
0.4%
36.4496015 1
0.4%
36.44724728 1
0.4%
36.44636624 1
0.4%
36.43553429 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct240
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.38801
Minimum127.30385
Maximum127.4636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T04:07:54.813043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.30385
5-th percentile127.31936
Q1127.35104
median127.38983
Q3127.42556
95-th percentile127.44523
Maximum127.4636
Range0.1597503
Interquartile range (IQR)0.0745166

Descriptive statistics

Standard deviation0.039906394
Coefficient of variation (CV)0.00031326648
Kurtosis-1.0193856
Mean127.38801
Median Absolute Deviation (MAD)0.0370716
Skewness-0.15546397
Sum30955.287
Variance0.0015925203
MonotonicityNot monotonic
2023-12-13T04:07:55.000776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.3337176 2
 
0.8%
127.4025901 2
 
0.8%
127.3193341 2
 
0.8%
127.3760129 1
 
0.4%
127.389185 1
 
0.4%
127.3873534 1
 
0.4%
127.3861735 1
 
0.4%
127.3887744 1
 
0.4%
127.3772864 1
 
0.4%
127.3789264 1
 
0.4%
Other values (230) 230
94.7%
ValueCountFrequency (%)
127.3038485 1
0.4%
127.3080603 1
0.4%
127.3120446 1
0.4%
127.3134695 1
0.4%
127.3174378 1
0.4%
127.3184062 1
0.4%
127.3185038 1
0.4%
127.3186257 1
0.4%
127.3189803 1
0.4%
127.3190059 1
0.4%
ValueCountFrequency (%)
127.4635988 1
0.4%
127.4616074 1
0.4%
127.4615355 1
0.4%
127.4609801 1
0.4%
127.4591998 1
0.4%
127.4568583 1
0.4%
127.4563917 1
0.4%
127.4553593 1
0.4%
127.4545256 1
0.4%
127.4510154 1
0.4%

위치
Text

MISSING 

Distinct240
Distinct (%)100.0%
Missing3
Missing (%)1.2%
Memory size2.0 KiB
2023-12-13T04:07:55.333215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length134
Median length108
Mean length75.195833
Min length26

Characters and Unicode

Total characters18047
Distinct characters15
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique240 ?
Unique (%)100.0%

Sample

1st row[1828197.2693,994246.9304],[1828328.1493,994147.2662],[1828035.1791,994211.6740]
2nd row[1828109.5279,993336.5531],[1828239.7033,993187.8689],[1828055.0119,993041.9357]
3rd row[1818853.4728,994275.1196],[1818649.7803,994495.3619]
4th row[1818159.2251,993327.7266],[1818185.0986,993625.4783]
5th row[1817815.1521,991963.1142],[1817603.6524,992175.8654]
ValueCountFrequency (%)
1813831.9476,992222.7052],[1813818.3298,992447.4693],[1813619.6596,992434.6781 1
 
0.4%
1818207.049,989065.5],[1818319.317,989167.354],[1818320.841,988947.898 1
 
0.4%
1811017.262,985451.969],[1811013.562,985151.96 1
 
0.4%
1818470.701,988933.547],[1818171.616,988932.594],[1818321.349,989057.372],[1818320.079,988779.877 1
 
0.4%
1817101.552,990441.831],[1817255.603,990600.327],[1817100.282,990733.232],[1816955.502,990599.057 1
 
0.4%
1817459.146,987936.533],[1817631.104,987791.753],[1817427.396,987647.989 1
 
0.4%
1817610.403,988856.5],[1817612.181,989154.696],[1817756.834,989008.646],[1817456.225,989009.662 1
 
0.4%
1816871.936,990148.588],[1817068.913,990147.826 1
 
0.4%
1816590.424,990529.015],[1816590.424,990229.015 1
 
0.4%
1816596.52,990210.499],[1816596.52,989910.499],[1816746.52,990060.499 1
 
0.4%
Other values (239) 239
96.0%
2023-12-13T04:07:55.768748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2400
13.3%
9 2152
11.9%
8 2040
11.3%
. 1402
7.8%
, 1162
 
6.4%
5 1121
 
6.2%
3 1119
 
6.2%
6 1111
 
6.2%
4 1082
 
6.0%
7 1059
 
5.9%
Other values (5) 3399
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14073
78.0%
Other Punctuation 2564
 
14.2%
Close Punctuation 701
 
3.9%
Open Punctuation 700
 
3.9%
Space Separator 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2400
17.1%
9 2152
15.3%
8 2040
14.5%
5 1121
8.0%
3 1119
8.0%
6 1111
7.9%
4 1082
7.7%
7 1059
7.5%
2 1039
7.4%
0 950
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 1402
54.7%
, 1162
45.3%
Close Punctuation
ValueCountFrequency (%)
] 701
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 700
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18047
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2400
13.3%
9 2152
11.9%
8 2040
11.3%
. 1402
7.8%
, 1162
 
6.4%
5 1121
 
6.2%
3 1119
 
6.2%
6 1111
 
6.2%
4 1082
 
6.0%
7 1059
 
5.9%
Other values (5) 3399
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2400
13.3%
9 2152
11.9%
8 2040
11.3%
. 1402
7.8%
, 1162
 
6.4%
5 1121
 
6.2%
3 1119
 
6.2%
6 1111
 
6.2%
4 1082
 
6.0%
7 1059
 
5.9%
Other values (5) 3399
18.8%

교차로수
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3497942
Minimum0
Maximum5
Zeros50
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T04:07:55.897887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4620085
Coefficient of variation (CV)0.62218577
Kurtosis-1.0033371
Mean2.3497942
Median Absolute Deviation (MAD)1
Skewness-0.44826736
Sum571
Variance2.137469
MonotonicityNot monotonic
2023-12-13T04:07:56.019908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 74
30.5%
4 56
23.0%
0 50
20.6%
2 46
18.9%
1 13
 
5.3%
5 4
 
1.6%
ValueCountFrequency (%)
0 50
20.6%
1 13
 
5.3%
2 46
18.9%
3 74
30.5%
4 56
23.0%
5 4
 
1.6%
ValueCountFrequency (%)
5 4
 
1.6%
4 56
23.0%
3 74
30.5%
2 46
18.9%
1 13
 
5.3%
0 50
20.6%

대표이미지
Text

Distinct52
Distinct (%)21.6%
Missing2
Missing (%)0.8%
Memory size2.0 KiB
2023-12-13T04:07:56.158994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.6970954
Min length1

Characters and Unicode

Total characters650
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)19.5%

Sample

1st row19
2nd row20
3rd row21
4th row24
5th row26
ValueCountFrequency (%)
1 161
48.5%
2 48
 
14.5%
3 45
 
13.6%
1,2 28
 
8.4%
1,2,3 5
 
1.5%
11 1
 
0.3%
200 1
 
0.3%
17 1
 
0.3%
16 1
 
0.3%
5 1
 
0.3%
Other values (40) 40
 
12.0%
2023-12-13T04:07:56.422426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 227
34.9%
, 129
19.8%
2 96
14.8%
91
14.0%
3 54
 
8.3%
8 14
 
2.2%
9 14
 
2.2%
0 6
 
0.9%
6 5
 
0.8%
7 5
 
0.8%
Other values (2) 9
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 430
66.2%
Other Punctuation 129
 
19.8%
Space Separator 91
 
14.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 227
52.8%
2 96
22.3%
3 54
 
12.6%
8 14
 
3.3%
9 14
 
3.3%
0 6
 
1.4%
6 5
 
1.2%
7 5
 
1.2%
5 5
 
1.2%
4 4
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 129
100.0%
Space Separator
ValueCountFrequency (%)
91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 227
34.9%
, 129
19.8%
2 96
14.8%
91
14.0%
3 54
 
8.3%
8 14
 
2.2%
9 14
 
2.2%
0 6
 
0.9%
6 5
 
0.8%
7 5
 
0.8%
Other values (2) 9
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 227
34.9%
, 129
19.8%
2 96
14.8%
91
14.0%
3 54
 
8.3%
8 14
 
2.2%
9 14
 
2.2%
0 6
 
0.9%
6 5
 
0.8%
7 5
 
0.8%
Other values (2) 9
 
1.4%

기준일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
20-08-31
178 
20-09-16
65 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20-08-31
2nd row20-08-31
3rd row20-08-31
4th row20-08-31
5th row20-08-31

Common Values

ValueCountFrequency (%)
20-08-31 178
73.3%
20-09-16 65
 
26.7%

Length

2023-12-13T04:07:56.560777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:07:56.654140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20-08-31 178
73.3%
20-09-16 65
 
26.7%

기타
Text

MISSING 

Distinct42
Distinct (%)100.0%
Missing201
Missing (%)82.7%
Memory size2.0 KiB
2023-12-13T04:07:56.865431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length24.5
Mean length19.428571
Min length5

Characters and Unicode

Total characters816
Distinct characters170
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row중리남로 40번길, 한밭대로 115번길 흰색선
2nd row한밭대로 994번길 흰색선
3rd row카메라 방향 애매
4th row오정로 75방향길 한쪽은 흰선
5th row문지 초 중 사이 도로 흰색선인대 카메라가 찍고 있음
ValueCountFrequency (%)
앞 9
 
5.2%
카메라 6
 
3.4%
지족동 5
 
2.9%
장대동 4
 
2.3%
흰색선 4
 
2.3%
한쪽은 4
 
2.3%
주변 3
 
1.7%
봉명동 3
 
1.7%
네거리 3
 
1.7%
없음 2
 
1.1%
Other values (121) 131
75.3%
2023-12-13T04:07:57.249640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
133
 
16.3%
동 25
 
3.1%
로 25
 
3.1%
) 20
 
2.5%
( 20
 
2.5%
6 16
 
2.0%
대 16
 
2.0%
4 15
 
1.8%
선 15
 
1.8%
길 13
 
1.6%
Other values (160) 518
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 514
63.0%
Space Separator 133
 
16.3%
Decimal Number 110
 
13.5%
Close Punctuation 20
 
2.5%
Open Punctuation 20
 
2.5%
Dash Punctuation 7
 
0.9%
Other Punctuation 7
 
0.9%
Lowercase Letter 4
 
0.5%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
 
4.9%
로 25
 
4.9%
대 16
 
3.1%
선 15
 
2.9%
길 13
 
2.5%
번 12
 
2.3%
색 11
 
2.1%
흰 11
 
2.1%
은 11
 
2.1%
지 10
 
1.9%
Other values (140) 365
71.0%
Decimal Number
ValueCountFrequency (%)
6 16
14.5%
4 15
13.6%
1 13
11.8%
9 13
11.8%
0 12
10.9%
3 11
10.0%
7 9
8.2%
2 9
8.2%
5 8
7.3%
8 4
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
s 1
25.0%
i 1
25.0%
g 1
25.0%
q 1
25.0%
Space Separator
ValueCountFrequency (%)
133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 514
63.0%
Common 298
36.5%
Latin 4
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
 
4.9%
로 25
 
4.9%
대 16
 
3.1%
선 15
 
2.9%
길 13
 
2.5%
번 12
 
2.3%
색 11
 
2.1%
흰 11
 
2.1%
은 11
 
2.1%
지 10
 
1.9%
Other values (140) 365
71.0%
Common
ValueCountFrequency (%)
133
44.6%
) 20
 
6.7%
( 20
 
6.7%
6 16
 
5.4%
4 15
 
5.0%
1 13
 
4.4%
9 13
 
4.4%
0 12
 
4.0%
3 11
 
3.7%
7 9
 
3.0%
Other values (6) 36
 
12.1%
Latin
ValueCountFrequency (%)
s 1
25.0%
i 1
25.0%
g 1
25.0%
q 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 514
63.0%
ASCII 302
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
44.0%
) 20
 
6.6%
( 20
 
6.6%
6 16
 
5.3%
4 15
 
5.0%
1 13
 
4.3%
9 13
 
4.3%
0 12
 
4.0%
3 11
 
3.6%
7 9
 
3.0%
Other values (10) 40
 
13.2%
Hangul
ValueCountFrequency (%)
동 25
 
4.9%
로 25
 
4.9%
대 16
 
3.1%
선 15
 
2.9%
길 13
 
2.5%
번 12
 
2.3%
색 11
 
2.1%
흰 11
 
2.1%
은 11
 
2.1%
지 10
 
1.9%
Other values (140) 365
71.0%

CCTV수
Categorical

Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1
170 
4
25 
3
23 
5
22 
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 170
70.0%
4 25
 
10.3%
3 23
 
9.5%
5 22
 
9.1%
0 3
 
1.2%

Length

2023-12-13T04:07:57.424521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:07:57.570642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 170
70.0%
4 25
 
10.3%
3 23
 
9.5%
5 22
 
9.1%
0 3
 
1.2%

Interactions

2023-12-13T04:07:50.121194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:48.589587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:49.148804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:49.592853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:50.251908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:48.716912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:49.254868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:49.740884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:50.376042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:48.865598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:49.359695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:49.871769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:50.502461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:49.029345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:49.485152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:07:50.004042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:07:57.685845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명시군구코드우편번호연락처위도경도교차로수대표이미지기준일자기타CCTV수
시군구명1.0001.0000.7230.9090.8630.9050.5880.8680.6841.0000.851
시군구코드1.0001.0000.7230.9090.8630.9050.5880.8680.6841.0000.851
우편번호0.7230.7231.0000.8920.7650.5810.4100.7380.1281.0000.526
연락처0.9090.9090.8921.0000.6890.6670.7710.8520.9541.0000.537
위도0.8630.8630.7650.6891.0000.6870.3760.8200.5451.0000.382
경도0.9050.9050.5810.6670.6871.0000.4510.5060.4871.0000.681
교차로수0.5880.5880.4100.7710.3760.4511.0000.7990.4381.0000.329
대표이미지0.8680.8680.7380.8520.8200.5060.7991.0000.5811.0000.000
기준일자0.6840.6840.1280.9540.5450.4870.4380.5811.0001.0000.310
기타1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
CCTV수0.8510.8510.5260.5370.3820.6810.3290.0000.3101.0001.000
2023-12-13T04:07:57.915128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연락처CCTV수시군구명시군구코드기준일자
연락처1.0000.4000.8570.8570.802
CCTV수0.4001.0000.4880.4880.375
시군구명0.8570.4881.0001.0000.812
시군구코드0.8570.4881.0001.0000.812
기준일자0.8020.3750.8120.8121.000
2023-12-13T04:07:58.076517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호위도경도교차로수시군구명시군구코드연락처기준일자CCTV수
우편번호1.000-0.6680.057-0.1190.5790.5790.4920.2050.372
위도-0.6681.000-0.1360.1970.5260.5260.4480.4130.166
경도0.057-0.1361.000-0.3320.5920.5920.4270.3680.345
교차로수-0.1190.197-0.3321.0000.4470.4470.3800.3130.230
시군구명0.5790.5260.5920.4471.0001.0000.8570.8120.488
시군구코드0.5790.5260.5920.4471.0001.0000.8570.8120.488
연락처0.4920.4480.4270.3800.8570.8571.0000.8020.400
기준일자0.2050.4130.3680.3130.8120.8120.8021.0000.375
CCTV수0.3720.1660.3450.2300.4880.4880.4000.3751.000

Missing values

2023-12-13T04:07:50.673470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:07:50.927599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T04:07:51.096867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

관리번호시군구명시군구코드우편번호주소연락처위도경도위치교차로수대표이미지기준일자기타CCTV수
03680000_CCTV_00019대덕구368000034309대전광역시 대덕구 신탄진북로 33042-608-528336.451213127.434912[1828197.2693,994246.9304],[1828328.1493,994147.2662],[1828035.1791,994211.6740]31920-08-31<NA>1
13680000_CCTV_00020대덕구368000034305대전광역시 대덕구 대덕대로 1591042-608-528336.450507127.424516[1828109.5279,993336.5531],[1828239.7033,993187.8689],[1828055.0119,993041.9357]32020-08-31<NA>1
23680000_CCTV_00021대덕구368000034407대전광역시 대덕구 계족산로81번길042-608-528336.364058127.443435[1818853.4728,994275.1196],[1818649.7803,994495.3619]42120-08-31<NA>1
33680000_CCTV_00024대덕구368000034395대전광역시 대덕구 중리남로 39042-608-528336.360708127.427407[1818159.2251,993327.7266],[1818185.0986,993625.4783]42420-08-31중리남로 40번길, 한밭대로 115번길 흰색선1
43680000_CCTV_00026대덕구368000034437대전광역시 대덕구 한밭대로1024번길 20042-608-528336.357431127.412409[1817815.1521,991963.1142],[1817603.6524,992175.8654]32620-08-31한밭대로 994번길 흰색선1
53680000_CCTV_00027대덕구368000034304대전광역시 대덕구 대덕대로 1555042-608-528336.450948127.42203[1828062.5440,992733.7991],[1827819.8719,992880.3306]42720-09-16카메라 방향 애매1
63680000_CCTV_00028대덕구368000034441대전광역시 대덕구 오정로75번길 1042-608-528336.352813127.409771[1817407.9699,991791.5333],[1817198.2371,992006.0348],[1817257.2411,991755.4546]52820-08-31오정로 75방향길 한쪽은 흰선1
73670000_CCTV_00183유성구367000034199대전광역시 유성구 상대동 467-18(상대초)042-609-285236.346198127.340106[1816797.1075,985098.9677],[1816647.1075,985248.9677],[1816797.1075,985398.9677]318320-09-16<NA>1
83670000_CCTV_00184유성구367000034195대전광역시 유성구 원신흥동 598(홍도초)042-609-285236.334079127.34015[1815289.1857,985326.3806],[1815139.1857,985476.3806],[1815289.1857,985626.3806],[1815439.1857,985476.3806]418420-09-16<NA>1
93670000_CCTV_00185유성구367000034194대전광역시 유성구 원신흥동 578-6(라도무스아트센터)042-609-285236.332959127.333978[1815007.3949,984855.1372],[1814857.3949,985005.1372],[1815157.3949,985005.1372],[1815007.3949,985155.1372]418520-09-16<NA>1
관리번호시군구명시군구코드우편번호주소연락처위도경도위치교차로수대표이미지기준일자기타CCTV수
2333660000_CCTV_00116서구366000035263대전광역시 서구 탄방로 8(탄방초등학교)042-609-285236.3461127.389963[1816533.5407,990304.9679],[1816390.7528,990148.0871],[1816683.8086,990149.0862]0120-08-31<NA>4
2343660000_CCTV_00117서구366000035368대전광역시 서구 도안동로 11번길(CGV 가수원)042-609-285236.306115127.353301[1812016.9024,986458.4734],[1812104.0632,986745.1145],[1812205.7454,986559.4495]0120-08-31<NA>4
2353660000_CCTV_00118서구366000035333대전광역시 서구 도마로 34(귀빈장마트)042-609-285236.322312127.373889[1814063.4428,988677.4349],[1813763.5272,988662.4909],[1813894.8184,988817.6743],[1813909.7927,988517.6743]0120-08-31<NA>5
2363660000_CCTV_00119서구366000035334대전광역시 서구 도마로 34(귀빈장마트)042-609-285236.319125127.373748[1813719.6453,988657.8231],[1813557.3338,988507.7922],[1813403.8318,988644.4187],[1813553.5903,988802.9277]0120-08-31<NA>5
2373660000_CCTV_00120서구366000035329대전광역시 서구 배재로 172번길 23(도마초등학교)042-609-285236.324146127.371719[1814208.3305,988431.3511],[1814045.8212,988577.4656],[1814052.7325,988369.5408]0120-08-31<NA>5
2383660000_CCTV_00121서구366000035368대전광역시 서구 계백로 1135(MK 클리닉)042-609-285236.304765127.349497[1812059.2107,986573.3165],[1812161.9151,986394.9755],[1812007.5198,986402.9161],[1811909.5416,986467.9458]0120-08-31<NA>3
2393660000_CCTV_00122서구366000035368대전광역시 서구 도안동로 3(리더스프라자)042-609-285236.326539127.355676[1811995.3527,986602.0694],[1812108.9128,986723.5142]0120-08-31<NA>3
2403660000_CCTV_00123서구366000035241대전광역시 서구 둔산남로 103(오투저축은행 대전본점)042-609-285236.349316127.389015[1816853.1044,989894.0417],[1817000.6751,990056.8180],[1816850.6921,990204.5641],[1816697.6041,990060.6760]0120-08-31<NA>5
2413660000_CCTV_00124서구366000035209대전광역시 서구 청사로 234(둥지네거리)042-609-285236.359513127.389837[1818049.1435,989979.2980],[1817885.0250,990140.9016],[1818192.1875,990140.3195],[1818042.1875,990290.3195]0120-08-31<NA>5
2423640000_CCTV_00032동구364000034629대전광역시 동구 대전로 792(중앙동)042-609-285236.33034127.433474[1814625.7652,994081.6969],[1814864.4373,993959.8989]0120-08-31<NA>1